mlsanityData to 14 Oct 2026

HyperTree Planning: Enhancing LLM Reasoning via Hierarchical Thinking

arXiv 2505.02322 ↗First posted 5 May 2025cs.AIcited by 14

On the map: LLM reasoning → Reasoning for agents, planning and code

Abstract (arXiv)

Recent advancements have significantly enhanced the performance of large language models (LLMs) in tackling complex reasoning tasks, achieving notable success in domains like mathematical and logical reasoning. However, these methods encounter challenges with complex planning tasks, primarily due to extended reasoning steps, diverse constraints, and the challenge of handling multiple distinct sub-tasks. To address these challenges, we propose HyperTree Planning (HTP), a novel reasoning paradigm that constructs hypertree-structured planning outlines for effective planning. The hypertree structure enables LLMs to engage in hierarchical thinking by flexibly employing the divide-and-conquer strategy, effectively breaking down intricate reasoning steps, accommodating diverse constraints, and managing multiple distinct sub-tasks in a well-organized manner. We further introduce an autonomous planning framework that completes the planning process by iteratively refining and expanding the hypertree-structured planning outlines. Experiments demonstrate the effectiveness of HTP, achieving state-of-the-art accuracy on the TravelPlanner benchmark with Gemini-1.5-Pro, resulting in a 3.6 times performance improvement over o1-preview.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 40 of its 72 references that are in the map. First posted on arXiv 5 May 2025.

Builds on

40 of 72 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents13 Aug
  2. 2023Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models6 Oct
  3. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate30 May
  4. 2023Reasoning with Language Model is Planning with World Model24 May

Other references · 36

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2024The Llama 3 Herd of Modelscited by 14,715
  3. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  4. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,808
  5. 2021What Makes Good In-Context Examples for GPT-3?cited by 1,195
  6. 2021Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivitycited by 1,135
  7. 2022Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Taskscited by 1,076
  8. 2023Large Language Models Cannot Self-Correct Reasoning Yetcited by 1,017
  9. 2017Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithmcited by 734
  10. 2022A Survey on In-context Learningcited by 721
  11. 2023Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Modelscited by 560
  12. 2024TravelPlanner: A Benchmark for Real-World Planning with Language Agentscited by 392
24 more
  1. 2024ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Searchcited by 344
  2. 2023Alphazero-like Tree-Search can Guide Large Language Model Decoding and Trainingcited by 295
  3. 2024To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoningcited by 263
  4. 2023AutoAgents: A Framework for Automatic Agent Generationcited by 216
  5. 2024Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solverscited by 138
  6. 2024NATURAL PLAN: Benchmarking LLMs on Natural Language Planningcited by 128
  7. 2023Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Datacited by 119
  8. 2024The Evolution of Statistical Induction Heads: In-Context Learning Markov Chainscited by 118
  9. 2024Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM Reasoningcited by 110
  10. 2024Chain of Preference Optimization: Improving Chain-of-Thought Reasoning in LLMscited by 96
  11. 2023Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Modelscited by 92
  12. 2022Solving Math Word Problems via Cooperative Reasoning induced Language Modelscited by 77
  13. 2023In-Context Learning with Iterative Demonstration Selectioncited by 74
  14. 2023Multilingual LLMs are Better Cross-lingual In-context Learners with Alignmentcited by 71
  15. 2023Dynamic Planning with a LLMcited by 61
  16. 2023Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Textcited by 59
  17. 2025Evolving Deeper LLM Thinkingcited by 58
  18. 2024Beyond Examples: High-level Automated Reasoning Paradigm in In-Context Learning via MCTScited by 54
  19. 2024Dual Operating Modes of In-Context Learningcited by 43
  20. 2024An Information-Theoretic Analysis of In-Context Learningcited by 40
  21. 2024Planning with Multi-Constraints via Collaborative Language Agentscited by 26
  22. 2024To the Globe (TTG): Towards Language-Driven Guaranteed Travel Planningcited by 21
  23. 2024Planning in Strawberry Fields: Evaluating and Improving the Planning and Scheduling Capabilities of LRM o1cited by 17
  24. 2024Transforming the Hybrid Cloud for Emerging AI Workloadscited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 7; 2026: 7.

14 papers.

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  1. 2026A Survey of Agentic Reasoning for Large Language Models: Towards Recursively Self-Improving and Collective Agentscited by 50
  2. 2025K2-Think: A Parameter-Efficient Reasoning Systemcited by 11
  3. 2025ReflecSched: Solving Dynamic Flexible Job-Shop Scheduling via LLM-Powered Hierarchical Reflectioncited by 5
  4. 2025AgentCoMa: A Compositional Benchmark Mixing Commonsense and Mathematical Reasoning in Real-World Scenarioscited by 3
  5. 2025MorphoBench: A Benchmark with Difficulty Adaptive to Model Reasoningcited by 2
  6. 2026Learning to Reason with Insight for Informal Theorem Provingcited by 2
  7. 2025TripScore: Aligning LLMs for Real-World Travel Planning via Expert-Calibrated Rewardcited by 1
  8. 2026Short Chains, Deep Thoughts: Balancing Reasoning Efficiency and Intra-Segment Capability via Split-Merge Optimizationcited by 1
  9. 2026Decoupled Travel Planning with Behavior Forestcited by 1
  10. 2025Entropy-Constrained Strategy Optimization in Urban Floods: A Multi-Agent Framework with LLM and Knowledge Graph Integrationcited by 0
  11. 2025VIR-Bench: Evaluating Geospatial and Temporal Understanding of MLLMs via Travel Video Itinerary Reconstructioncited by 0
  12. 2026Knowing What to Solve Before How: Preplan Empowered LLM Mathematical Reasoningcited by 0
2 more
  1. 2026Sampling via Decision-Flow: Training-Free Extraction of Improved Latent Reasoning Paths in Large Language Modelscited by 0
  2. 2026HyperMCTS: Hypergraph-Augmented MCTS for Long-Horizon LLM Agentscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025A Roadmap to Guide the Integration of LLMs in Hierarchical Planning
  2. 2024Thinking Forward and Backward: Effective Backward Planning with Large Language Models
  3. 2026Revisiting the Travel Planning Capabilities of Large Language Models
  4. 2025Policy Guided Tree Search for Enhanced LLM Reasoning
  5. 2024A Human-Like Reasoning Framework for Multi-Phases Planning Task with Large Language Models
  6. 2026SokoBench: Evaluating Long-Horizon Planning and Reasoning in Large Language Models
  7. 2025Decompose, Plan in Parallel, and Merge: A Novel Paradigm for Large Language Models based Planning with Multiple Constraints
  8. 2025Large Language Models for Planning: A Comprehensive and Systematic Survey